Introduction

We will Analyze a Time Series Dataset in R.

Case Study -1: Walmart Sales Dataset from `timetk`

Let us inspect what datasets are available in the package timetk. Type data(package = "timetk") in your Console to see what datasets are available.

Let us choose the Walmart Sales dataset. See here for more details: Walmart Recruiting - Store Sales Forecasting |Kaggle


data("walmart_sales_weekly")
walmart_sales_weekly
#> # A tibble: 1,001 × 17
#>   id    Store  Dept Date       Weekly…¹ IsHol…² Type    Size Tempe…³ Fuel_…⁴ MarkD…⁵ MarkD…⁶ MarkD…⁷ MarkD…⁸ MarkD…⁹   CPI Unemp…˟
#>   <fct> <dbl> <dbl> <date>        <dbl> <lgl>   <chr>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <dbl>   <dbl>
#> 1 1_1       1     1 2010-02-05   24924. FALSE   A     151315    42.3    2.57      NA      NA      NA      NA      NA  211.    8.11
#> 2 1_1       1     1 2010-02-12   46039. TRUE    A     151315    38.5    2.55      NA      NA      NA      NA      NA  211.    8.11
#> 3 1_1       1     1 2010-02-19   41596. FALSE   A     151315    39.9    2.51      NA      NA      NA      NA      NA  211.    8.11
#> 4 1_1       1     1 2010-02-26   19404. FALSE   A     151315    46.6    2.56      NA      NA      NA      NA      NA  211.    8.11
#> # … with 997 more rows, and abbreviated variable names ¹Weekly_Sales, ²IsHoliday, ³Temperature, ⁴Fuel_Price, ⁵MarkDown1,
#> #   ⁶MarkDown2, ⁷MarkDown3, ⁸MarkDown4, ⁹MarkDown5, ˟Unemployment
inspect(walmart_sales_weekly)
#> 
#> categorical variables:  
#>        name     class levels    n missing                                  distribution
#> 1        id    factor   3331 1001       0 1_1 (14.3%), 1_3 (14.3%), 1_8 (14.3%) ...    
#> 2 IsHoliday   logical      2 1001       0 FALSE (93%), TRUE (7%)                       
#> 3      Type character      1 1001       0 A (100%)                                     
#> 
#> Date variables:  
#>   name class      first       last min_diff max_diff    n missing
#> 1 Date  Date 2010-02-05 2012-10-26   0 days   7 days 1001       0
#> 
#> quantitative variables:  
#>            name   class         min          Q1      median          Q3         max         mean           sd    n missing
#> 1         Store numeric      1.0000      1.0000      1.0000      1.0000      1.0000 1.000000e+00 0.000000e+00 1001       0
#> 2          Dept numeric      1.0000      3.0000     13.0000     93.0000     95.0000 3.585714e+01 3.849159e+01 1001       0
#> 3  Weekly_Sales numeric   6165.7300  28257.3000  39886.0600  77943.5700 148798.0500 5.464634e+04 3.627627e+04 1001       0
#> 4          Size numeric 151315.0000 151315.0000 151315.0000 151315.0000 151315.0000 1.513150e+05 0.000000e+00 1001       0
#> 5   Temperature numeric     35.4000     57.7900     69.6400     80.4900     91.6500 6.830678e+01 1.420767e+01 1001       0
#> 6    Fuel_Price numeric      2.5140      2.7590      3.2900      3.5940      3.9070 3.219699e+00 4.260286e-01 1001       0
#> 7     MarkDown1 numeric    410.3100   4039.3900   6154.1400  10121.9700  34577.0600 8.090766e+03 6.550983e+03  357     644
#> 8     MarkDown2 numeric      0.5000     40.4800    144.8700   1569.0000  46011.3800 2.941315e+03 7.873661e+03  294     707
#> 9     MarkDown3 numeric      0.2500      6.0000     25.9650    101.6400  55805.5100 1.225400e+03 7.811934e+03  350     651
#> 10    MarkDown4 numeric      8.0000    577.1400   1822.5500   3750.5900  32403.8700 3.746085e+03 5.948867e+03  357     644
#> 11    MarkDown5 numeric    554.9200   3127.8800   4325.1900   6222.2500  20475.3200 5.018655e+03 3.254071e+03  357     644
#> 12          CPI numeric    210.3374    211.5312    215.4599    220.6369    223.4443 2.159969e+02 4.337818e+00 1001       0
#> 13 Unemployment numeric      6.5730      7.3480      7.7870      7.8380      8.1060 7.610420e+00 3.825958e-01 1001       0
glimpse(walmart_sales_weekly)
#> Rows: 1,001
#> Columns: 17
#> $ id           <fct> 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_1, 1_…
#> $ Store        <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ Dept         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ Date         <date> 2010-02-05, 2010-02-12, 2010-02-19, 2010-02-26, 2010-03-05, 2010-03-12, 2010-03-19, 2010-03-26, 2010-04-02…
#> $ Weekly_Sales <dbl> 24924.50, 46039.49, 41595.55, 19403.54, 21827.90, 21043.39, 22136.64, 26229.21, 57258.43, 42960.91, 17596.9…
#> $ IsHoliday    <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
#> $ Type         <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A…
#> $ Size         <dbl> 151315, 151315, 151315, 151315, 151315, 151315, 151315, 151315, 151315, 151315, 151315, 151315, 151315, 151…
#> $ Temperature  <dbl> 42.31, 38.51, 39.93, 46.63, 46.50, 57.79, 54.58, 51.45, 62.27, 65.86, 66.32, 64.84, 67.41, 72.55, 74.78, 76…
#> $ Fuel_Price   <dbl> 2.572, 2.548, 2.514, 2.561, 2.625, 2.667, 2.720, 2.732, 2.719, 2.770, 2.808, 2.795, 2.780, 2.835, 2.854, 2.…
#> $ MarkDown1    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ MarkDown2    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ MarkDown3    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ MarkDown4    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ MarkDown5    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ CPI          <dbl> 211.0964, 211.2422, 211.2891, 211.3196, 211.3501, 211.3806, 211.2156, 211.0180, 210.8204, 210.6229, 210.488…
#> $ Unemployment <dbl> 8.106, 8.106, 8.106, 8.106, 8.106, 8.106, 8.106, 8.106, 7.808, 7.808, 7.808, 7.808, 7.808, 7.808, 7.808, 7.…

# Try this in your Console
# help("walmart_sales_weekly")

The data is described as:

A tibble: 9,743 x 3

id Factor. Unique series identifier (4 total)

Store Numeric. Store ID.

Dept Numeric. Department ID.

Date Date. Weekly timestamp.

Weekly_Sales Numeric. Sales for the given department in the given store.

IsHoliday Logical. Whether the week is a “special” holiday for the store.

Type Character. Type identifier of the store.

Size Numeric. Store square-footage

Temperature Numeric. Average temperature in the region.

Fuel_Price Numeric. Cost of fuel in the region.

MarkDown1, MarkDown2, MarkDown3, MarkDown4, MarkDown5 Numeric. Anonymized data related to promotional markdowns that Walmart is running. MarkDown data is only available after Nov 2011, and is not available for all stores all the time. Any missing value is marked with an NA.

CPI Numeric. The consumer price index.

Unemployment Numeric. The unemployment rate in the region.

Very cool to know that mosaic::inspect() identifies date variables separately!

NOTE: 1. This is still a data.frame, with a time-oriented variable of course, and not yet a time-series object. Note that this data frame has the YMD columns repeated for each Dept. 2. The Date column has repeated entries for each Dept! To deal with this repetition, we will always need to split the Weekly_Sales by the Dept column before we plot or analyze.

Since our sales are weekly, we will convert Date to yearweek format:


walmart_time <- walmart_sales_weekly %>% 
  # mutate(Date = as.Date(Date)) %>% 
  as_tsibble(index = Date, # Time Variable
             key = Dept 
             
  #  Identifies unique "subject" who are measures
  #  All other variables such as Weekly_sales become "measured variable"
  #  Each observation should be uniquely identified by index and key

             )
walmart_time
#> # A tsibble: 1,001 x 17 [7D]
#> # Key:       Dept [7]
#>   id    Store  Dept Date       Weekly…¹ IsHol…² Type    Size Tempe…³ Fuel_…⁴ MarkD…⁵ MarkD…⁶ MarkD…⁷ MarkD…⁸ MarkD…⁹   CPI Unemp…˟
#>   <fct> <dbl> <dbl> <date>        <dbl> <lgl>   <chr>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <dbl>   <dbl>
#> 1 1_1       1     1 2010-02-05   24924. FALSE   A     151315    42.3    2.57      NA      NA      NA      NA      NA  211.    8.11
#> 2 1_1       1     1 2010-02-12   46039. TRUE    A     151315    38.5    2.55      NA      NA      NA      NA      NA  211.    8.11
#> 3 1_1       1     1 2010-02-19   41596. FALSE   A     151315    39.9    2.51      NA      NA      NA      NA      NA  211.    8.11
#> 4 1_1       1     1 2010-02-26   19404. FALSE   A     151315    46.6    2.56      NA      NA      NA      NA      NA  211.    8.11
#> # … with 997 more rows, and abbreviated variable names ¹Weekly_Sales, ²IsHoliday, ³Temperature, ⁴Fuel_Price, ⁵MarkDown1,
#> #   ⁶MarkDown2, ⁷MarkDown3, ⁸MarkDown4, ⁹MarkDown5, ˟Unemployment

Basic Time Series Plots

The easiest way is to use autoplot from the feasts package. You may need to specify the actual measured variable, if there is more than one numerical column:


autoplot(walmart_time,.vars = Weekly_Sales)

timetk gives us interactive plots that may be more evocative than the static plot above. The basic plot function with timetk is plot_time_series. There are arguments for the date variable, the value you want to plot, colours, groupings etc.

Let us explore this dataset using timetk, using our trusted method of asking Questions:

Q.1 How are the weekly sales different for each Department?

There are 7 number of Departments. So we should be fine plotting them and also facetting with them, as we will see in a bit:


walmart_time %>%   timetk::plot_time_series(Date, Weekly_Sales,
                   .color_var = Dept, .legend_show = TRUE,
                   .title = "Walmart Sales Data by Department",
                   .smooth = FALSE)

Q.2. What do the sales per Dept look like during the month of December (Christmas time) in 2012? Show the individual Depts as facets.

We can of course zoom into the interactive plot above, but if we were to plot it anyway:


# Only include rows from  1 to December 31, 2011
# Data goes only up to Oct 2012

walmart_time %>% 
  # Each side of the time_formula is specified as the character 'YYYY-MM-DD HH:MM:SS',
  timetk::filter_by_time(.date_var = Date,
                         .start_date = "2011-12-01",
                         .end_date = "2011-12-31") %>%

  plot_time_series(.date_var = Date, 
                   .value = Weekly_Sales, 
                   .color_var = Dept, 
                   .facet_vars = Dept, 
                   .facet_ncol = 2,
                   .smooth = FALSE) # Only 4 points per graph

Clearly the “unfortunate” Dept#13 has seen something of a Christmas drop in sales, as has Dept#38 ! The rest, all is well, it seems…

Too much noise? How about some averaging?

Q.3 How do we smooth out some of the variations in the time series to be able to understand it better?

Sometimes there is too much noise in the time series observations and we want to take what is called a rolling average. For this we will use the function timetk::slidify to create an averaging function of our choice, and then apply it to the time series using regular dplyr::mutate


# Let's take the average of Sales for each month in each Department.
# Our **function** will be named "rolling_avg_month": 

rolling_avg_month = slidify(.period = 4, # every 4 weeks
                            .f = mean, # The funtion to average
                            .align = "center", # Aligned with middle of month
                            .partial = TRUE) # TO catch any leftover half weeks
rolling_avg_month
#> function (...) 
#> {
#>     slider_2(..., .slider_fun = slider::pslide, .f = .f, .period = .period, 
#>         .align = .align, .partial = .partial, .unlist = .unlist)
#> }
#> <bytecode: 0x000002b5ca1b5ff0>
#> <environment: 0x000002b5ca1b56c0>

OK, slidify creates a function! Let’s apply it to the Walmart Sales time series…


walmart_time %>% 
  # group_by(Dept) %>% 
  mutate(avg_monthly_sales = rolling_avg_month(Weekly_Sales)) %>% 
  # ungroup() %>% 
  timetk::plot_time_series(Date, avg_monthly_sales,.color_var = Dept, .smooth = FALSE)

Curves are smoother now. Need to check whether the averaging was done on a per-Dept basis…should we have had a group_by(Dept) before the averaging, and ungroup() before plotting? Try it !!

Decomposing Time Series: Trends, Seasonal Patterns, and Cycles

Each data point (\(Y_t\)) at time \(t\) in a Time Series can be expressed as either a sum or a product of 4 components, namely, Seasonality(\(S_t\)), Trend(\(T_t\)), Cyclic, and Error(\(e_t\)) (a.k.a White Noise).

Trend: pattern exists when there is a long-term increase or decrease in the data.
Seasonal: pattern exists when a series is influenced by seasonal factors (e.g., the quarter of the year, the month, or day of the week).
Cyclic: pattern exists when data exhibit rises and falls that are not of fixed period (duration usually of at least 2 years).
Error or Noise: Random component

Decomposing non-seasonal datas means breaking it up into trend and irregular components.To estimate the trend component of a non-seasonal time series that can be described using an additive model, it is common to use a smoothing method, such as calculating the simple moving average of the time series.

timetk has the ability to achieve this: Let us plot the trend, seasonal, cyclic and irregular aspects of Weekly_Sales for Dept 38:


walmart_time %>% 
  filter(Dept == "38") %>% 
  timetk::plot_stl_diagnostics(.date_var = Date, .value = Weekly_Sales)
#> frequency = 13 observations per 1 quarter
#> trend = 52 observations per 1 year

We can do this for all Dept using fable and fabletools:


walmart_decomposed <- 
  walmart_time %>% 
  
  # If we want to filter, we do it here
  # filter(Dept == "38") %>% 
  # 

  fabletools::model(stl = STL(Weekly_Sales))

fabletools::components(walmart_decomposed)
#> # A dable: 1,001 x 8 [7D]
#> # Key:     Dept, .model [7]
#> # :        Weekly_Sales = trend + season_year + remainder
#>    Dept .model Date       Weekly_Sales  trend season_year remainder season_adjust
#>   <dbl> <chr>  <date>            <dbl>  <dbl>       <dbl>     <dbl>         <dbl>
#> 1     1 stl    2010-02-05       24924. 23138.        469.     1318.        24456.
#> 2     1 stl    2010-02-12       46039. 23129.      17545.     5366.        28495.
#> 3     1 stl    2010-02-19       41596. 23120.      24371.    -5895.        17225.
#> 4     1 stl    2010-02-26       19404. 23111.      -3270.     -437.        22673.
#> # … with 997 more rows

autoplot(components((walmart_decomposed)))

Case Study-2: Dataset from `nycflights13`

Let us try the flights dataset from the package nycflights13. Try data(package = "nycflights13") in your Console.

We have the following datasets innycflights13:

Data sets in package nycflights13:

airlines Airline names.
airports Airport metadata
flights Flights data
planes Plane metadata.
weather Hourly weather data

Let us analyze the flights data:


data("flights", package = "nycflights13")
mosaic::inspect(flights)
#> 
#> categorical variables:  
#>      name     class levels      n missing                                  distribution
#> 1 carrier character     16 336776       0 UA (17.4%), B6 (16.2%), EV (16.1%) ...       
#> 2 tailnum character   4043 334264    2512 N725MQ (0.2%), N722MQ (0.2%) ...             
#> 3  origin character      3 336776       0 EWR (35.9%), JFK (33%), LGA (31.1%)          
#> 4    dest character    105 336776       0 ORD (5.1%), ATL (5.1%), LAX (4.8%) ...       
#> 
#> quantitative variables:  
#>              name   class  min   Q1 median   Q3  max        mean          sd      n missing
#> 1            year integer 2013 2013   2013 2013 2013 2013.000000    0.000000 336776       0
#> 2           month integer    1    4      7   10   12    6.548510    3.414457 336776       0
#> 3             day integer    1    8     16   23   31   15.710787    8.768607 336776       0
#> 4        dep_time integer    1  907   1401 1744 2400 1349.109947  488.281791 328521    8255
#> 5  sched_dep_time integer  106  906   1359 1729 2359 1344.254840  467.335756 336776       0
#> 6       dep_delay numeric  -43   -5     -2   11 1301   12.639070   40.210061 328521    8255
#> 7        arr_time integer    1 1104   1535 1940 2400 1502.054999  533.264132 328063    8713
#> 8  sched_arr_time integer    1 1124   1556 1945 2359 1536.380220  497.457142 336776       0
#> 9       arr_delay numeric  -86  -17     -5   14 1272    6.895377   44.633292 327346    9430
#> 10         flight integer    1  553   1496 3465 8500 1971.923620 1632.471938 336776       0
#> 11       air_time numeric   20   82    129  192  695  150.686460   93.688305 327346    9430
#> 12       distance numeric   17  502    872 1389 4983 1039.912604  733.233033 336776       0
#> 13           hour numeric    1    9     13   17   23   13.180247    4.661316 336776       0
#> 14         minute numeric    0    8     29   44   59   26.230100   19.300846 336776       0
#> 
#> time variables:  
#>        name   class               first                last min_diff   max_diff      n missing
#> 1 time_hour POSIXct 2013-01-01 05:00:00 2013-12-31 23:00:00   0 secs 25200 secs 336776       0

We have time-related columns; Apart from year, month, day we have time_hour; and time-event numerical data such as arr_delay (arrival delay) and dep_delay (departure delay). We also have categorical data such as carrier, origin, dest, flight and tailnum of the aircraft. It is also a large dataset containing 330K entries. Enough to play with!!

Let us replace the NAs in arr_delay and dep_delay with zeroes for now, and convert it into a time-series object with tsibble:


flights_delay_ts <- flights %>% 
  
  mutate(arr_delay = replace_na(arr_delay, 0), 
         dep_delay = replace_na(dep_delay, 0)) %>% 
  
  select(time_hour, arr_delay, dep_delay, carrier, origin, dest, flight, tailnum) %>% 
  tsibble::as_tsibble(index = time_hour, 
                      # All the remaining identify unique entries
                      # Along with index
                      # Many of these variables are common
                      # Need *all* to make unique entries!
                      key = c(carrier, origin, dest,flight, tailnum), 
                      validate = TRUE) # Making sure each entry is unique


flights_delay_ts
#> # A tsibble: 336,776 x 8 [1h] <America/New_York>
#> # Key:       carrier, origin, dest, flight, tailnum [186,870]
#>   time_hour           arr_delay dep_delay carrier origin dest  flight tailnum
#>   <dttm>                  <dbl>     <dbl> <chr>   <chr>  <chr>  <int> <chr>  
#> 1 2013-05-04 07:00:00       -11        -6 9E      EWR    ATL     3633 N170PQ 
#> 2 2013-05-01 11:00:00       -24        -5 9E      EWR    ATL     4194 N170PQ 
#> 3 2013-05-03 09:00:00        15        -6 9E      EWR    ATL     4362 N170PQ 
#> 4 2013-05-02 09:00:00        -5        -6 9E      EWR    ATL     4362 N232PQ 
#> # … with 336,772 more rows

Q.1. Plot the monthly average arrival delay by carrier


mean_arr_delays_by_carrier <- 
  flights_delay_ts %>%
  group_by(carrier) %>% 
  
  index_by(month = ~ yearmonth(.)) %>% 
  # index_by uses (year, yearquarter, yearmonth, yearweek, as.Date)
  # to create a new column to show the time-grouping
  # year / quarter / month/ week, or day...
  # which IS different from traditional dplyr
  
  summarise(mean_arr_delay = mean(arr_delay, na.rm = TRUE)
  )

mean_arr_delays_by_carrier
#> # A tsibble: 185 x 3 [1M]
#> # Key:       carrier [16]
#>   carrier    month mean_arr_delay
#>   <chr>      <mth>          <dbl>
#> 1 9E      2013 Jan           9.60
#> 2 9E      2013 Feb           7.61
#> 3 9E      2013 Mar           1.89
#> 4 9E      2013 Apr           5.07
#> # … with 181 more rows

mean_arr_delays_by_carrier %>%
  timetk::plot_time_series(
    .date_var = month,
    .value = mean_arr_delay,
    .facet_vars = carrier,
    .smooth = FALSE,
    # .smooth_degree = 1,
    
    # keep .smooth off since it throws warnings if there are too few points
    # Like if we do quarterly or even yearly summaries
    # Use only for smaller values of .smooth_degree (0,1)
    #
    .facet_ncol = 4,
    .title = "Average Monthly Arrival Delays by Carrier"
  )

Q.2. Plot a candlestick chart for total flight delays by month for each carrier


flights_delay_ts %>% 
  mutate(total_delay = arr_delay + dep_delay) %>%
  timetk::plot_time_series_boxplot(
    .date_var = time_hour,
    .value = total_delay,
    .color_var = origin,
    .facet_vars = origin,
    .period = "month",
  # same warning again
    .smooth = FALSE
  )

Q.2. Plot a heatmap chart for total flight delays by origin, aggregated by month


avg_delays_month <- flights_delay_ts %>% 
  group_by(origin) %>% 
  mutate(total_delay = arr_delay + dep_delay) %>% 
  index_by(month = ~ yearmonth(.)) %>% 
  # index_by uses (year, yearquarter, yearmonth, yearweek, as.Date)
  # to create a new column to show the time-grouping
  # year / quarter / month/ week, or day...
  # which IS different from traditional dplyr
    summarise(mean_monthly_delay = mean(total_delay, na.rm = TRUE)
  )

avg_delays_month 
#> # A tsibble: 36 x 3 [1M]
#> # Key:       origin [3]
#>   origin    month mean_monthly_delay
#>   <chr>     <mth>              <dbl>
#> 1 EWR    2013 Jan               27.0
#> 2 EWR    2013 Feb               20.6
#> 3 EWR    2013 Mar               27.7
#> 4 EWR    2013 Apr               30.7
#> # … with 32 more rows
# three origins 12 months therefore 36 rows
# Tsibble index_by + summarise also gives us a  month` column 



ggformula::gf_tile(origin ~ month, fill = ~ mean_monthly_delay, 
                   color = "black", data = avg_delays_month,
                   title = "Mean Flight Delays from NY Airports in 2013") %>% 
  gf_theme(theme_classic()) %>% 
  gf_theme(scale_fill_viridis_c(option = "A")) %>% 
  
# "magma" (or "A") inferno" (or "B") "plasma" (or "C") 
# "viridis" (or "D") "cividis" (or "E") 
# "rocket" (or "F") "mako" (or "G") "turbo" (or "H")

  gf_theme(scale_x_time(breaks = 1:12, labels = month.abb))

Introduction

Case Study -1: Walmart Sales Dataset from `timetk`

Basic Time Series Plots

Too much noise? How about some averaging?

Decomposing Time Series: Trends, Seasonal Patterns, and Cycles

Case Study-2: Dataset from `nycflights13`

Conclusion

References

Introduction

Case Study -1: Walmart Sales Dataset from timetk

Basic Time Series Plots

Too much noise? How about some averaging?

Decomposing Time Series: Trends, Seasonal Patterns, and Cycles

Case Study-2: Dataset from nycflights13

Conclusion

References

Case Study -1: Walmart Sales Dataset from `timetk`

Case Study-2: Dataset from `nycflights13`